In this course you will learn how to apply the functional programming style in the design of larger applications. You'll get to know important new functional programming concepts, from lazy evaluation to structuring your libraries using monads. We'll work on larger and more involved examples, from state space exploration to random testing to discrete circuit simulators. You’ll also learn some best practices on how to write good Scala code in the real world.
Several parts of this course deal with the question how functional programming interacts with mutable state. We will explore the consequences of combining functions and state. We will also look at purely functional alternatives to mutable state, using infinite data structures or functional reactive programming.
Learning Outcomes. By the end of this course you will be able to:
- recognize and apply design principles of functional programs,
- design functional libraries and their APIs,
- competently combine functions and state in one program,
- understand reasoning techniques for programs that combine
functions and state,
- write simple functional reactive applications.
Recommended background: You should have at least one year programming experience. Proficiency with Java or C# is ideal, but experience with other languages such as C/C++, Python, Javascript or Ruby is also sufficient. You should have some familiarity using the command line. This course is intended to be taken after Functional Programming Principles in Scala: https://www.coursera.org/learn/progfun1.

次における5の2コース

100%オンライン

自分のスケジュールですぐに学習を始めてください。

柔軟性のある期限

柔軟性のある期限

スケジュールに従って期限をリセットします。

中級レベル

中級レベル

修了時間

約13時間で修了

利用可能な言語

英語

字幕：英語

シラバス - 本コースの学習内容

週

1

修了時間

8時間で修了

For Expressions and Monads

We'll start by revisiting some concepts that we have learned from Principles of Functional Programming in Scala; collections, pattern matching, and functions. We'll then touch on for-comprehensions, a powerful way in Scala to traverse a list, process it, and return a new list. We'll see how to do queries with for-comprehensions as well as how the for-comprehension is "desugared" into calls to higher-order functions by the Scala compiler. Finally, we'll discuss what monads are, and how to verify that the monad laws are satisfied for a number of examples.

Lazy Evaluation

This week we'll revisit performance issues caused by combinatorial search, and we'll discover an important concept in functional programming that can these issues: laziness. We'll also learn a little bit about proofs on trees; in particular, we'll see how to extend structural induction to trees.

Functions and State

This week, we'll learn about state and side-effects. Through a rich example, we'll learn programming patterns for managing state in larger programs. We'll also learn about for-loops and while-loops in Scala.

Timely Effects

This week we'll learn a number of important programming patterns via examples, starting with the observer pattern, and then going on to functional reactive programming. We'll learn how latency can be modeled as an effect, and how latency can be handled with Scala's monadic futures. We'll learn the important combinators on futures as well as how they can be composed to build up rich and responsive services.

講師

スイス連邦工科大学ローザンヌ校（École Polytechnique Fédérale de Lausanne）について

Functional Programming in Scala専門講座について

Discover how to write elegant code that works the first time it is run.
This Specialization provides a hands-on introduction to functional programming using the widespread programming language, Scala. It begins from the basic building blocks of the functional paradigm, first showing how to use these blocks to solve small problems, before building up to combining these concepts to architect larger functional programs. You'll see how the functional paradigm facilitates parallel and distributed programming, and through a series of hands on examples and programming assignments, you'll learn how to analyze data sets small to large; from parallel programming on multicore architectures, to distributed programming on a cluster using Apache Spark. A final capstone project will allow you to apply the skills you learned by building a large data-intensive application using real-world data....